His primary areas of study are Artificial intelligence, Artificial neural network, Recurrent neural network, Mathematical optimization and Pattern recognition. His Artificial intelligence research incorporates themes from Machine learning, Dynamic programming and Computer vision. His study in the field of Backpropagation and Backpropagation through time is also linked to topics like Elementary function.
His Recurrent neural network research is multidisciplinary, incorporating perspectives in Speech recognition, Exponential stability, Extended Kalman filter and Electroencephalography. Danil V. Prokhorov works mostly in the field of Mathematical optimization, limiting it down to topics relating to Reinforcement learning and, in certain cases, Reinforcement, Implementation and Optimal control. In the subject of general Pattern recognition, his work in Feature extraction, Wavelet transform and Convolutional neural network is often linked to Proximal Gradient Methods, thereby combining diverse domains of study.
His main research concerns Artificial intelligence, Artificial neural network, Computer vision, Recurrent neural network and Machine learning. His work on Pattern recognition expands to the thematically related Artificial intelligence. His work carried out in the field of Artificial neural network brings together such families of science as Kalman filter and Control system.
His is doing research in Video tracking, Cognitive neuroscience of visual object recognition, Tracking and Contextual image classification, both of which are found in Computer vision. His work investigates the relationship between Recurrent neural network and topics such as Control theory that intersect with problems in Control. His study brings together the fields of Mathematical optimization and Adaptive control.
His scientific interests lie mostly in Artificial intelligence, Acoustics, Actuator, Computer vision and Artificial neural network. Danil V. Prokhorov has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His study explores the link between Machine learning and topics such as Inference that cross with problems in Pixel.
His Acoustics research incorporates elements of Medial surface and Aerodynamics. His study in Artificial neural network is interdisciplinary in nature, drawing from both Control system and Mathematical optimization. His studies deal with areas such as Classifier, Recurrent neural network, Image segmentation and Brake as well as Convolutional neural network.
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Convolutional neural network, Simulation and Pattern recognition. His Artificial intelligence study combines topics in areas such as Machine learning, Vehicle dynamics and Computer vision. The various areas that Danil V. Prokhorov examines in his Computer vision study include Control and Headlamp.
In his study, Sensitivity is inextricably linked to Artificial neural network, which falls within the broad field of Deep learning. Danil V. Prokhorov regularly links together related areas like Recurrent neural network in his Convolutional neural network studies. His work deals with themes such as Boosting, Task analysis and Feature, which intersect with Pattern recognition.
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Adaptive critic designs
D.V. Prokhorov;D.C. Wunsch.
IEEE Transactions on Neural Networks (1997)
Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks
E.W. Saad;D.V. Prokhorov;D.C. Wunsch.
IEEE Transactions on Neural Networks (1998)
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking
Zhibin Hong;Zhe Chen;Chaohui Wang;Xue Mei.
computer vision and pattern recognition (2015)
Remote management of vehicle settings
Setu Madhavi Namburu;Steven F. Kalik;Danil V. Prokhorov.
Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene
Jun Li;Xue Mei;Danil Prokhorov;Dacheng Tao.
IEEE Transactions on Neural Networks (2017)
Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG
Arthur Petrosian;Danil V. Prokhorov;Richard Homan;Richard Dasheiff.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
Fan Yang;Lei Zhang;Sijia Yu;Danil Prokhorov.
IEEE Transactions on Intelligent Transportation Systems (2020)
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Derong Liu;Murad Abu-Khalaf;Adel M. Alimi;Charles Anderson.
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Zhiqiang Wan;Hepeng Li;Haibo He;Danil Prokhorov.
IEEE Transactions on Smart Grid (2019)
Adaptive critic designs: a case study for neurocontrol
Danil V. Prokhorov;Roberto A. Santiago;Donald C. Wunsch.
Neural Networks (1995)
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